From John Snow to omics: the long journey of environmental epidemiology

A major difference between infectious and non-communicable diseases is that infectious diseases typically have unique necessary causes whereas noncommunicable diseases have multiple causes which by themselves are usually neither necessary nor sufficient. Epidemiology seems to have reached a limit in disentangling the role of single components in causal complexes, particularly at low doses. To overcome limitations the discipline can take advantage of technical developments including the science of the exposome. By referring to the interpretation of the exposome as put forward in the work of Wild and Rappaport, I show examples of how the science of multi-causality can build upon the developments of omic technologies. Finally, I broaden the picture by advocating a more holistic approach to causality that also encompasses social sciences and the concept of embodiment. To tackle NCDs effectively on one side we can invest in various omic approaches, to identify new external causes of non-communicable diseases (that we can use to develop preventive strategies), and the corresponding mechanistic pathways. On the other side, we need to focus on the social and societal determinants which are suggested to be the root causes of many non-communicable diseases.

From John Snow to omics: the long journey of environmental epidemiology

European Journal of Epidemiology
From John Snow to omics: the long journey of environmental epidemiology
Paolo Vineis 0 1
Omics Exposomics Epigenetics Socially-transmitted 0 1
0 Italian Institute for Genomic Medicine , Turin , Italy
1 & Paolo Vineis
A major difference between infectious and non-communicable diseases is that infectious diseases typically have unique necessary causes whereas noncommunicable diseases have multiple causes which by themselves are usually neither necessary nor sufficient. Epidemiology seems to have reached a limit in disentangling the role of single components in causal complexes, particularly at low doses. To overcome limitations the discipline can take advantage of technical developments including the science of the exposome. By referring to the interpretation of the exposome as put forward in the work of Wild and Rappaport, I show examples of how the science of multi-causality can build upon the developments of omic technologies. Finally, I broaden the picture by advocating a more holistic approach to causality that also encompasses social sciences and the concept of embodiment. To tackle NCDs effectively on one side we can invest in various omic approaches, to identify new external causes of non-communicable diseases (that we can use to develop preventive strategies), and the corresponding mechanistic pathways. On the other side, we need to focus on the social and societal determinants which are suggested to be the root causes of many non-communicable diseases.
Non-communicable disease conditions
-
Among the heroes of modern epidemiology, John Snow is
probably the most popular from the viewpoint of both
epidemiologists and the lay public. By removing the handle
of the Broad Street Pump in London, Snow allegedly
stopped the epidemic of cholera (though the story is
slightly more complex; for a better account see [
1
]). This
public health gesture was supported by parallel work on
mapped results that indicated that distributions of the
diseased followed the patterns of water provision from water
companies (http://www.ph.ucla.edu/epi/snow/snowbook3.
html). Another hero in epidemiology, Max von
Pettenkofer, initiated a public health revolution by improving
sanitation and housing in Munich. Pettenkofer was an
anticontagionist, i.e. he did not believe that the then
predominant infectious diseases could be attributed to germs.
Though anti-contagionists were wrong, they have attracted
MRC-PHE Centre for Environment and Health, Imperial
College London, Norfolk Place, London W21PG, UK
much attention and favour because of the effectiveness of
their preventive practices, which were focused—to use a
now popular expression—on the ‘‘causes of causes’’. It is
relevant to postulate whether these and other early
movements for healthy cities in the nineteenth century can be
replicated in reaction to the current epidemics of so-called
‘‘non-communicable diseases’’ (NCDs). Thus, in this
commentary the analogies between concepts from the
period of Snow and Pettenkofer and today’s challenges
posed by understanding NCDs spreading in the world are
discussed.
The reasoning behind this paper can be summarized as
follows: a major difference between infectious and
noncommunicable diseases is that infectious diseases typically
have unique necessary causes whereas non-communicable
diseases typically have many causes which by themselves
are usually neither necessary nor sufficient. Epidemiology
seems to have reached a limit in disentangling the role of
single components in causal complexes, and to overcome
limitations the discipline can take advantage of technical
developments including the science of the exposome. By
referring to the interpretation of the exposome as put
forward in the work of Wild and Rappaport, I show
examples of how the science of multi-causality can build
upon the developments of omic technologies. Finally, I
broaden the picture by advocating a more holistic approach
to causality that also encompasses social sciences and the
concept of embodiment.
Multicausality: real or due to lack of knowledge?
By simplifying Snow’s story, we can say that the ‘‘Snow
manoeuvre’’ (the closure of the pump) was successful for a
number of reasons that do not apply to NCDs: Vibrio (not
known at the time if we exclude the work of Filippo Pacini)
was a unique and necessary cause of cholera, i.e. the
interruption of the causal chain was allowed because of a
single act. However, if we consider the current leading
diseases in the world according to the Burden of Disease
programme, they are not due to necessary causes. For
example, hypertension arises as a consequence of a
network of determinants including excessive salt in one’s diet.
The well-known characteristics of NCDs have led to the
basic concept of NCD epidemiology, multifactoriality as
epitomized by ‘‘Rothman’s pies’’ [
2
]. Causation is
interpreted as a chain of events or a constellation of exposures
where none by itself is able to cause the disease. This
model has far reaching consequences for the theories of
causality. In the case of cancer, for example, it is
hypothesized that several mutations, epimutations or ‘‘hallmarks
of cancer’’ [
3
] are needed to complete a causal chain from
exposure to disease, though the exact number of events is
unknown. This implies that in any population there are
some individuals who may be especially predisposed to
developing cancer because of inherited genetic variations,
mutations induced by carcinogenic substances, epigenetic
changes, and/or other ‘‘hallmarks of cancer’’. A small
number of people in the population may lack the activation
of just the last stage or hit to complete the carcinogenic
process—even at low exposure doses, while a larger
number may lack the activation of two stages, three stages,
etc. Luckier individuals (but often based on their lower
exposures to risk factors) have no activated stage. With
exceptions (HPV and cervical cancer being the most
obvious one), we do not know single necessary causes for
any NCD. Then the question arises: is this a consequence
of our ignorance or is it a fact?
Ignorance, as an explanation of the web of causation, is
similar to the unawareness that existed before the
discoveries of microbiology when diseases were classified based
on the symptoms, while with technological developments
(microscope, laboratory glassware, enriched cultures) the
discovery of single infectious agents led to a dramatic
reclassification (e.g. some ‘‘fevers’’ became TB and others
malaria). Therefore, according to the ‘‘ignorance’’ thesis
we might be on the edge of a new revolution in our
knowledge of NCDs, should a technological development
allow us to isolate single necessary causes for
well-identified, redefined disease entities.
This seems unlikely to me. The last 50 years of
epidemiology have seen the greatest effort in history to
identify the causes of cancer and other NCDs, with many
successes (mainly coming from epidemiology) and also
much frustration. It seems that we are now accepting the
multicausality paradigm, by which there are far reaching
implications. For example, when the International Agency
for Research on Cancer (IARC) Working Groups
concluded that secondhand smoke, processed red meat and
ambient air pollution are carcinogenic to humans, they also
noted that for the dose–response relationship no threshold
was evident. This in turn implies that all three agents are
likely to act synergistically with other exposures, i.e.
neither agent is a single necessary cause of cancer. In brief, we
accept that something is carcinogenic to humans when it
can act at low doses and is not necessary nor sufficient to
induce the disease. We also know from extensive research
that each of the three exposures can cause other NCDs: for
example, meat intake is associated with cardiovascular
diseases and air pollution with cardiovascular, respiratory
and perhaps neurological diseases. This line of reasoning
seems to be the only one supported by current evidence but
is not accepted by people with vested interests, including
toxicologists sponsored by industries, who seem to wait for
the next revolution in causality that will put things in order
with NCDs.
In practice, if we accept that there is no alternative to the
multicausality model for NCDs, we need to develop tools
that will allow us to investigate networks and pathways to
lend credibility to causal chains, to allow the detection of
early changes at low dose levels, and to study synergies
between exposures and components of mixtures. Also, we
need to understand how early exposures can leave marks
that may impact health outcomes after decades, like in the
case of the Dutch famine and its impact later on in life (see
below). I believe that the concept of the exposome and the
associated technologies provide such tools, as exemplified
in the Exposomics project (http://www.exposomicsproject.
eu). In support of this, I discuss a few examples where the
exposome can be used to inform causality, and particularly
for NCDs. What follows does not mean to be an exhaustive
description of what the exposome science is or might be,
but rather provide some examples of contributions in key
aspects of the multi-causality conundrum.
The science of multicausality: early findings from Exposomics
Omics indicate early molecular changes at low levels of exposure
The epidemiology of NCDs has been struggling with how
to determine effects of exposures at low doses. There is no
evidence that common exposures such as air pollution or
secondhand smoking show a threshold in their action. For
example, in the ESCAPE network we found that mortality
increases below the current thresholds set by WHO [
4
].
The effects of low or very low doses may be due to genetic
susceptibility, as researchers have argued for years, but in
fact genetic epidemiology so far has been unable to find
sets of gene variants that are strongly associated with
increased susceptibility (except for familial conditions such
as BRCA1 mutations). Even in the case of smoking and
lung cancer, the additional susceptibility conferred by gene
variants tends to be modest [5]. Another explanation of low
dose effects, as suggested above, is the combination of
different exposures conferring acquired susceptibility.
The existence of low dose effects implies that we should
be able to detect molecular changes at those levels of
exposure. Support for this is starting to appear through the
application of ‘omics in investigations of low dose
exposures. For example, we have found that the levels of
exposure to PM10 experienced in utero from four different
European areas (Fig. 1a) influence cord blood metabolomic
signals (Fig. 1b). This can be perceived at low levels of
exposure to air pollution, though we also note that the
effects are stronger in the areas with higher levels of
pollution than in rural areas with lower levels (unpublished).
Omics suggest that different pollutants in a mixture may lead to different biological pathways, but perhaps not always
Do components in a mixture act separately (via different
metabolic or molecular pathways) or do they impact
common pathways? Omics investigations shown in Figs. 2
and 3 suggest that both cases are realistic. Figure 2 refers
to the Oxford Street randomized cross-over trial [
6
] in
which volunteers were exposed to high (Oxford Street,
London) or low (Hyde Park) levels of air pollution. The
figure shows that both for RNA (gene expression and
circulating miRNA, involved in gene expression modulation)
and for metabolites from mass spectrometry the different
components of air pollution give rise to signals that do not
overlap, suggesting that each pollutant (except perhaps
PM2.5 and PM10) follows a different metabolic or
molecular pathway to exert its effects. Again, these are low
or very low levels of exposure. There are limitations in our
ability to identify clear pathways, particularly in
metabolomics due to still poor annotation of signals, but Fig. 2
seems to open an interesting avenue for research.
The example shown in Fig. 3 is opposite. In this case we
enrolled swimmers (volunteers) to swim in a normal
Barcelona pool contaminated by chlorinated or brominated
disinfection-by-products [
7
]. In this example there seems to
be a broad overlap between miRNA and, respectively, MS
metabolomic signals across five different disinfection
byproducts.
Omics suggest that meaningful biological pathways connect exposure and outcome
The IARC Monographs use a strict procedural approach to
the evaluation of causality, as explained in their Preamble
[
8
]. They use criteria very similar to the Bradford Hill
guidelines used by epidemiologists for decades and derived
from Henle–Koch’s postulates for infectious diseases.
However, an extension of Henle–Koch’s postulates to
NCDs is not straightforward, since they stated that ‘‘1. The
agent must be demonstrable in every case of the
disease; 2. The agent is not present in other diseases; 3. After
isolation in culture, the agent must be able to produce the
disease in experimental animals’’. Clearly this is at odds
with what we have said about NCDs, and the postulates
have been modified by Bradford Hill to be adapted to the
multifactorial nature of NCDs. Among Bradford Hill’s
guidelines there is also reproducibility in animals and
biological plausibility, two criteria extensively applied in
the IARC Monographs to establish causality. For example,
red meat intake has been associated with exposure to at
least four different groups of carcinogenic substances
[
9, 10
].
One way to lend credibility to a causal interpretation of
epidemiological associations is to look for intermediate
steps that link exposure and disease, an approach we have
called ‘‘meet-in-the-middle’’. In the case of air pollution,
many studies (and in particular the ESCAPE network) have
demonstrated an impact on cardiovascular diseases (CVD),
and several studies also an impact of air pollution on
inflammatory and oxidative stress markers, but none has
linked the three components together, i.e. exposure,
intermediate mechanisms and outcome. In Exposomics we
designed a case–control study on CVD nested in the EPIC
cohort. We measured air pollution, inflammatory
biomarkers, and whole-genome DNA methylation in blood
collected up to 17 years before the diagnosis. We identified
enrichment of altered DNA methylation in
‘ROS/Glutathione/Cytotoxic granules’ and ‘Cytokine signaling’
pathways related genes, associated with both air pollution
and with CVD risk [
11
]. Our findings indicate that chronic
exposure to air pollution can cause oxidative stress, which
in turn activates a cascade of inflammatory responses
mainly involving the ‘Cytokine signaling’ pathway,
leading to increased risk of CVD. Inflammatory proteins and
DNA methylation alterations can be detected several years
before CVD diagnosis in blood samples, being promising
pre-clinical biomarkers. Figure 4 summarizes our
‘‘meetin-the-middle’’ reasoning.
Epigenetics suggests that stem cells may have long term memory of environmental challenges
Exposures in early life may leave a long-lasting trace in the
body, with effects that can manifest after many decades.
This concept was at the basis of Barker’s hypothesis [
12
]
and of the current developmental origin of health and
disease (DOHaD) theory [
13
]. After 60 years after being
exposed to the Dutch famine in utero individuals showed
epigenetic changes in genes including IGF-2 [
14
] (though
the evidence is far from conclusive). These observations
have been made from circulating white blood cell DNA,
and the only way to make sense of them is that epigenetic
changes are transmitted through generations of cells via
stem cells. We know that some exposures alter the
structure of DNA causing mutations, but there is increasing
evidence that the same or other environmental exposures,
including those that occur during foetal development in
utero, can cause epigenetic effects that modulate gene
expression without altering DNA structure. Some of such
epigenetic changes are at least partially reversible, but
other epigenetic modifications seem to persist even for
decades. In addition to the Dutch famine, probably the best
example is tobacco smoking. In a series of epigenome-wide
association studies we have investigated the dynamics of
smoking-induced epigenetic changes after smoking
cessation. Two distinct classes of CpG sites were identified: sites
whose methylation reverted to levels typical of never
smokers within decades after smoking cessation, and sites
remaining differentially methylated, even more than
35 years after smoking cessation [
15
]. This and other
similar studies highlight persistent epigenetic markers of
smoking, which can potentially be detected decades after
cessation.
To explain the long-term persistence of epigenetic
modifications, such as DNA methylation, we proposed an
analogy with immune memory. We proposed that an
epigenetic memory can be established and maintained in
selfrenewing stem cell compartments. We suggested that the
observations concerning early life effects on adult diseases
(the Dutch famine) and the persistence of methylation
changes in ex-smokers support our hypothesis. Although
epigenetic changes seem to be mainly adaptive, they are
also likely implicated in the pathogenesis and onset of
diseases. Elucidating the relationships between the
adaptive and maladaptive consequences of the epigenetic
modifications that result from complex environmental
exposures is a major challenge for current and future
research in epidemiology and epigenetics [
16
].
Like the methods of Snow and Pettenkofer today’s
epidemiologist uses patterns to suggest potential cause and
effect relationships, before clear mechanisms underlying
associations between exposures and outcomes are defined.
However, unlike at their time, as we have argued here, we
have a new toolbox to expand our methodologies and to
better support causality with potential mechanisms,
particular for NCDs. However, there are still considerable
underlying challenges that we face in understanding the
full picture of causality for NCDs, as I try to argue in the
next paragraph.
Fig. 3 In the Piscina study transcriptomic signals (a), miRNAs
(b) and metabolomic signals (c) overlap across different disinfection
by-products (DBP) in a swimming pool. Venn diagrams show
Bonferroni significant hits, adjusted for sex, age and BMI.
The most difficult challenge of causality and NCDs: to connect natural with social sciences
What are NCDs? A recent debate in Lancet Global Health,
sparked by Allen and Feigl, focused on the ambiguous and
unclear nature of NCDs [
17
]. According to the authors,
‘‘The current list of NCDs describes a ragtag group of
leftovers that do not satisfy Koch’s postulates’’. The
attributes of this mixed bag are, among others: chronicity;
global burden; preventable nature; common proximal risk
CHCl3 = chloroform; BDCM = bromodichloromethane; DBCM =
dibromochloromethane; Br3CH = bromoform (N = 41) (Espin
et al.[
24
] and Van Veldhoven et al. [
7
])
factors (cholesterol, blood pressure, glucose, obesity);
common behavioural risk factors (smoking, alcohol, diet,
inactivity, etc.); common distal risk factors (economic,
social, environmental); common issues of inequality and
injustice. Given these shared properties and the ambiguous
nature of their current denomination (NCDs), Allen and
Feigl suggest we call them ‘‘Socially-Transmitted
Conditions’’ (STC). This is an interesting (and controversial)
move, but leads to an even more difficult challenge, i.e.
how we connect the ‘‘social’’ and the ‘‘natural’’, the study
of society and the study of bodies and molecules to
investigate the causes of NCDs. This problem has been
named by Nancy Krieger the embodiment of social
relationships [
18
]. Once again, we suggest that the concepts
and tools of exposomics can be instrumental in
accomplishing this goal. One example of how omics (namely,
epigenomics) can connect social determinants of health
with molecular changes is in the association between ‘‘age
acceleration’’ and socio-economic status.
Low socioeconomic position (SEP) has been associated
with earlier onset of age-related chronic conditions and
reduced life-expectancy. We have investigated the
association of SEP with DNA methylation age acceleration (AA)
in more than 5000 individuals belonging to three
independent prospective cohorts from Italy, Australia, and
Ireland [
19
]. AA is based on a discrepancy between
chronological age and the level of methylation of a number
of CpG islands in DNA, a consistent indicator of biological
ageing. Low SEP was associated with greater AA and the
association was only partially modulated by the unhealthy
lifestyle habits of individuals with lower SEP. Individuals
who experienced life-course SEP improvement had
intermediate AA compared to extreme SEP categories,
supporting the relative importance of early childhood
social environment [
19
].
This example is only one among other examples of
embodiment, and a suite of indicators has been proposed
and used in our Lifepath network on SEP-related ageing
trajectories (www.lifepathproject.eu), e.g. allostatic load,
inflammatory biomarkers, metabolomics, proteomics [
20
]
and transcriptomics [
21
]. Also, the conceptual
understanding of the complex relationships between SEP (an
overarching determinant), risk factors for NCDs, molecular
and metabolic mechanisms and health outcomes is far from
being understood and cannot be tackled with simplistic or
reductionist explanations [
22
].
Conclusions
There are two main messages in what precedes. First,
something equivalent to the ‘‘Snow manoeuvre’’ is unlikely
to be realistic for NCDs. NCDs are different from
infectious diseases indeed. Infectious diseases, in general
(not always), are due to necessary agents that are very
specific (e.g. Mycobacterium for TB, Vibrio for cholera,
HIV for AIDS, etc.). Specificity is such that also medical
preventive measures (i.e. vaccines) are disease-specific.
Rather than single causes with short induction periods in
NCDs we are looking for complex webs of causation, in
which multiple factors—including at low doses—are
involved; and embodiment of social relationships and
social structure is likely to be a key concept. To respond to
these challenges the traditional tools of epidemiology are
inadequate. Like the substantial progress in the
identification of causes and the classification of infectious diseases
were propelled by the development of new tools, including
the microscope, now the new technologies (largely
molecular biology and mass spectrometry) may allow
important steps forward in NCD epidemiology. However,
we have to be aware that technological advancements are
never sufficient in the absence of a clear conceptual
framework to interpret them. Two of such frameworks
(obviously mutually compatible) are the theory of
‘‘embodiment’’ [
18
] and the concept of socially-transmitted
conditions [
17
].
In fact, and this is the second point I want to make,
anticontagionists were right in saying (and demonstrating) that
‘‘systemic’’ societal interventions in improving the health
of cities had multiple benefits: sanitation and fresh water
led to a decline of a number of water-borne diseases, not
just one or a few. Improvements of air and housing quality
were also accompanied by similar broad, wide-ranging
benefits. This is clearly still true for NCDs (or
sociallytransmitted conditions), as they tend to share the same, or
at least part of the same risk factors. This concept is not
only true for physical health but also for mental health.
Despite vast research in the area, we know little of the
etiologies underlying mental health (e.g. depression, the
leading cause of disability in high-income countries),
however it is likely that similar improvements and systemic
interventions would improve mental health on a societal
level. As housing density and sanitation have led to
successes in Pettenkofer’s Munich, nowadays city planning
and the organization of life including leisure time may
have much to do with the development of NCDs and
adverse mental health.
In conclusion, it is likely that to tackle NCDs effectively
on one side we need to invest in various omic approaches,
to identify new external causes of non-communicable
diseases that we can use to develop preventive strategies. On
the other side, we need to focus much more on the social
and societal determinants which are suggested to be the
root causes of many non-communicable diseases.
Acknowledgements This work has been supported by the
Exposomics (European Commission FP7 Grant Number 308610) and
Lifepath (European Commission H2020 Grant Number 633666)
grants. I am grateful to Jessica Laine and Augustin Scalbert for
thoughtful comments. Presented at the workshop ‘‘Setting research
priorities in environment and health’’. WHO, Bonn, 30 November
2017.
Open Access This article is distributed under the terms of the Creative
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commons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
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